The dataset consists of training and test data and label matrices for single-pixel compressive DoA estimation for mmWave metasurface. The dataset will be uploaded soon. Currently, a small part of the dataset can be accessed through this repository. For detailed information, please visit Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation through https://arxiv.org/abs/2109.05466

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This is a CSI dataset towards 5G NR high-precision positioning,

which is fine-grainedgeneral-purpose and 3GPP R16 standards-complied.

 

5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).

Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G  NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.

Instructions: 

 

The dataset_[SNR]_[date]_[time].mat contains: 

1) a 4-D matrix, features, representing the feature data, and

2) a structure array, labels, labeling the ground truth of UE positions.

[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.

The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).

The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.

The value of Ng corresponds to the number of UEs in labels.

 

 Colsed beta test is running.

In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.

 

In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.

 

Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.

To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.

 

 Release note 

2021-07-23 :

1) Recruit participants for colsed beta test.

2021-07-22 :

1)Expend our dataset with more CSI data with low SNR levels noise.

2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.

Closed beta test will start after suggestion collection.

2021-07-18 :

1)Expend our dataset with more CSI data with different SNR levels noise.

2)Publish map files for Scenario 1 indoor office.

 

 

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622 Views

This dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling

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216 Views

The emerging 5G services offer numerous new opportunities for networked applications. In this study, we seek to answer two key questions: i) is the throughput of mmWave 5G predictable, and ii) can we build "good" machine learning models for 5G throughput prediction? To this end, we conduct a measurement study of commercial mmWave 5G services in a major U.S. city, focusing on the throughput as perceived by applications running on user equipment (UE).

Instructions: 

DATASET WEBSITE: https://lumos5g.umn.edu/

## OVERVIEW

Lumos5G 1.0 is a dataset that represents the `Loop` area of the IMC'20 paper - "Lumos5G: Mapping and Predicting Commercial mmWave 5G Throughput". The Loop area is a 1300 meter loop near U.S. Bank Stadium in Minneapolis downtown area that covers roads, railroad crossings, restaurants, coffee shops, and recreational outdoor parks.

This dataset is being made available to the research community.

## DATASET COLUMNS AND DESCRIPTION

The description of the columns in the dataset CSV, from left to right, are:

- `run_num`: Indicates the run number. For each trajectory and mobility mode, we conduct several runs of experiments.
- `seq_num`: This is the sequence number. For each run, the sequence number acts like an index or a per-second timeline.
- `abstractSignalStr`: Indicates the abstract signal strength as reported by Android API (https://developer.android.com/reference/android/telephony/SignalStrength...()). No matter whether the UE was connected to 5G service or not, this column always reported a value associated with the LTE/4G radio. Note, if one is interested to understand the signal strength values related to 5G-NR, we refer them to other columns such as `nr_ssRsrp`, `nr_ssRsrq`, and `nr_ssSinr`.
- `latitude`: The latitude in degrees as reported by Android's API (https://developer.android.com/reference/android/location/Location#getLat...()).
- `longitude`: The longitude in degrees as reported by Android's API (https://developer.android.com/reference/android/location/Location#getLon...()).
- `movingSpeed`: The ground mobility/moving speed of the UE as reported by Android's API (https://developer.android.com/reference/android/location/Location#getSpeed()). The unit is meters per second.
- `compassDirection`: The bearing in degrees as reported by Android's API (https://developer.android.com/reference/android/location/Location#getBea...()). Bearing is the horizontal direction of travel of this device, and is not related to the device orientation. It is guaranteed to be in the range `(0.0, 360.0]` if the device has a bearing.
- `nrStatus`: Indicates if the UE was connected to 5G network or not. When `nrStatus=CONNECTED`, the UE was connected to 5G. All other values of `nrStatus` such as `NOT_RESTRICTED` and `NONE` indicate the UE was not connected to 5G. `nrStatus` was obtained by parsing the raw string representation of `ServiceState` object (https://developer.android.com/reference/android/telephony/ServiceState#t...()).
- `lte_rssi`: Get Received Signal Strength Indication (RSSI) in dBm of the primary serving LTE cell. The value range is [-113, -51] inclusively or CellInfo#UNAVAILABLE if unavailable. Reference: TS 27.007 8.5 Signal quality +CSQ.
- `lte_rsrp`: Get reference signal received power (RSRP) in dBm of the primary serving LTE cell.
- `lte_rsrq`: Get reference signal received quality (RSRQ) of the primary serving LTE cell.
- `lte_rssnr`: Get reference signal signal-to-noise ratio (RSSNR) of the primary serving LTE cell.
- `nr_ssRsrp`: Obtained by parsing the raw string representation of `SignalStrength` object (https://developer.android.com/reference/android/telephony/SignalStrength...()). `nr_ssRsrp` was a field in this object's `CellSignalStrengthNr` section. In general, this value was only available when the UE was connected to 5G (i.e., when `nrStatus=CONNECTED`). Reference: 3GPP TS 38.215. Range: -140 dBm to -44 dBm.
- `nr_ssRsrq`: Obtained by parsing the raw string representation of `SignalStrength` object (https://developer.android.com/reference/android/telephony/SignalStrength...()). `nr_ssRsrq` was a field in this object's `CellSignalStrengthNr` section. In general, this value was only available when the UE was connected to 5G (i.e., when `nrStatus=CONNECTED`). Reference: 3GPP TS 38.215. Range: -20 dB to -3 dB.
- `nr_ssSinr`: Obtained by parsing the raw string representation of `SignalStrength` object (https://developer.android.com/reference/android/telephony/SignalStrength...()). `nr_ssSinr` was a field in this object's `CellSignalStrengthNr` section. In general, this value was only available when the UE was connected to 5G (i.e., when `nrStatus=CONNECTED`). Reference: 3GPP TS 38.215 Sec 5.1.*, 3GPP TS 38.133 10.1.16.1 Range: -23 dB to 40 dB
- `Throughput`: Indicates the throughput perceived by the UE. iPerf 3.7 was used to measure the per-second TCP downlink at the UE.
- `mobility_mode`: Indicates the grouth truth about the mobility mode when the experiment was conducted. This value can either be walking or driving.
- `trajectory_direction`: Indicates the ground truth about the trajectory direction of the experiment conducted at the Loop area. `CW` indicates clockwise direction, while `ACW` indicates anti-clockwise. Note, the driving experiments were only conducted in `CW` direction as certain parts of the loop were one way only. Walking-based experiments were conducted in both directions.
- `tower_id`: Indicates the (anonymized) tower identifier.

Note: We found that availability (and at times even the values) of `lte_rssi`, `nr_ssRsrp`, `nr_ssRsrq` and `nr_ssSinr` were not reliable. Since these values were sampled every second, at certain times (e.g., boundary cases), we might still find NR-related values when `nrStatus` is not equal to `CONNECTED`. However, in this dataset, we still include all the raw values as reported by the APIs.

## CITING THE DATASET

```
@inproceedings{10.1145/3419394.3423629,
author = {Narayanan, Arvind and Ramadan, Eman and Mehta, Rishabh and Hu, Xinyue and Liu, Qingxu and Fezeu, Rostand A. K. and Dayalan, Udhaya Kumar and Verma, Saurabh and Ji, Peiqi and Li, Tao and Qian, Feng and Zhang, Zhi-Li},
title = {Lumos5G: Mapping and Predicting Commercial MmWave 5G Throughput},
year = {2020},
isbn = {9781450381383},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3419394.3423629},
doi = {10.1145/3419394.3423629},
booktitle = {Proceedings of the ACM Internet Measurement Conference},
pages = {176–193},
numpages = {18},
keywords = {bandwidth estimation, mmWave, machine learning, Lumos5G, throughput prediction, deep learning, prediction, 5G},
location = {Virtual Event, USA},
series = {IMC '20}
}
```

## QUESTIONS?

Please feel free to contact the FiveGophers/Lumos5G team for questions or information about the data (arvind@cs.umn.edu,eman@cs.umn.edu,zhzhang@cs.umn.edu,fengqian@umn.edu,fivegophers@umn.edu)

## LICENSE

Lumos5G 1.0 dataset is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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235 Views

We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band 5G carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance, and explore the feasibility of using location and possibly other environmental information to predict the network performance.

Instructions: 

DATASET WEBSITE: https://fivegophers.umn.edu/www20/

## OVERVIEW

5Gophers 1.0 is a dataset collected when the world's very first commercial 5G services were made available to consumers. It should serve as a baseline to evaluate the 5G's performance evolution over time. Results using this dataset is presented in our measurement paper - "A First Look at Commercial 5G Performance on Smartphones".

This dataset is being made available to the research community.

## FILES and FOLDER STRUCTURE

All the files are in CSV format with headers that should hopefully be self-explainatory.

5Gophers-v1.0
├── All-Carriers
│   ├── 01-Throughput
│   ├── 02-Round-Trip-Times
│   └── 03-User-Mobility
└── mmWave-only
├── 03-UE-Panel (LoS Tests)
├── 04-Ping-Traces (Latency Tests)
├── 05-UE-Panel (NLoS Tests)
├── 06-UE-Panel (Orientation Tests)
├── 07-UE-Panel (Distance Tests)
├── 08-Web-Page-Load-Tests
├── 09-HTTPS-CDN-vs-NonCDN (Download Test)
└── 10-HTTP-vs-HTTPS (Download Test)

## CITING THE DATASET

```
@inproceedings{10.1145/3366423.3380169,
author = {Narayanan, Arvind and Ramadan, Eman and Carpenter, Jason and Liu, Qingxu and Liu, Yu and Qian, Feng and Zhang, Zhi-Li},
title = {A First Look at Commercial 5G Performance on Smartphones},
year = {2020},
isbn = {9781450370233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3366423.3380169},
doi = {10.1145/3366423.3380169},
booktitle = {Proceedings of The Web Conference 2020},
pages = {894–905},
numpages = {12},
location = {Taipei, Taiwan},
series = {WWW ’20}
}
```

## QUESTIONS?

Please feel free to contact the FiveGophers team for information about the data (fivegophers@umn.edu, naray111@umn.edu)

## LICENSE

5Gophers 1.0 dataset is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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194 Views

This is the dataset of mmWave massive MIMO beamspace channels, which is used for the experiment implementation of the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems". The source code of the experiment implementation is also open-access on the Github repository DeepBP-AE

Instructions: 

1. The "DeepMIMO_dataset.mat" is the spatial-domain channel dataset that can be reproduced by running "DeepMIMO_Dataset_Generator.m". For more details, refer to the public DeepMIMO dataset "DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications". 

2. The "H_beam_sparsity_syn3.mat" is the beamspace-domain channel dataset that can be reproduced by running "deepMIMO_beamspace_channels.m". 

3. For more detailed parameter settings, please refer to the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems" (currently under review).

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488 Views

This pdf file contains supplementary information to a published article, "Early Warning of mmWave Signal Blockage and AoA Transition Using sub-6 GHz Observations," by Ziad Ali, Alexandra Duel-Hallen and Hans Hallen, published in IEEE Communications Letters, 2019.

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139 Views

Data and codes for journal paper "MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning" submitted to IEEE Access.

The code assumes Python 3.

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499 Views

This database includes data measured by Qualcomm's 60GHz mmWave Radar.

It includes:

Face signature data base: radar face scan data of 206 individuals for face recognition. 

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1081 Views